When the matching cost function in Semiglobal Matching is unstable, the inaccurate matching cost values will be propagated in the cost aggregation process. It will lead to a serious mismatching phenomenon. To address the problem, a binocular images dense matching method considering image adaptive color weights and feature points was proposed. Firstly, The Color Birchfield Tomasi (CBT) matching cost calculation method was proposed to obtain a stable initial cost volume, which combined image adaptive color weights and gradient information. Secondly, the Scale-invariant Feature Transform matching algorithm was used to extract the a priori feature points from binocular images. Then, the feature points were filtrated. The cost volume was optimized by using their coordinate information and disparity information. Finally, an aggregation path segmentation rectification method was adopted to optimize the SGM aggregation paths and reduce the propagation of incorrect paths. Experimental results demonstrate that the proposed method can effectively improve the stability and accuracy of dense matching, reduce the mismatching phenomenon, and finally produce high-quality disparity maps.

Point-based sparse or dense matching can typically obtain satisfactory 3D point clouds of general contour features, but the deformation problem at the edges of artificial objects is prominent. Thus, a quasi-dense matching algorithm for close-range images combined with feature line constraint is proposed in this study. The method utilizes reliable matched points to construct the initial Delaunay triangulation and then optimizes the triangulation using the matched feature line. On this basis, iterative quasi-dense matching based on triangulation constraint is implemented. The process achieves matching with the center of the inscribed circle of each triangle as the seed point and growing matching. Two sets of stereo image pairs acquired using smartphones and four sets of sequence images provided by public datasets are selected for quasi-dense matching experiments. The comparison of results of constraint matching of the two triangulations before and after optimization as well as the matching results obtained via VisualSFM software demonstrated that the 3D point cloud obtained via quasi-dense matching with feature line constraint presents better results at the edges of buildings, thereby confirming the effectiveness of the proposed algorithm.INDEX TERMS Quasi-dense matching, Delaunay triangulation, close-range image, image matching, feature line constraint.